class_docstr: Data augmentation method in `InstaBoost: Boosting Instance
  Segmentation Via Probability Map Guided Copy-Pasting
  <https://arxiv.org/abs/1908.07801>`_.
  
  Refer to https://github.com/GothicAi/Instaboost for implementation details.
  
  
  Required Keys:
  
  - img (np.uint8)
  - instances
  
  Modified Keys:
  
  - img (np.uint8)
  - instances
  
  Args:
      action_candidate (tuple): Action candidates. "normal", "horizontal", \
          "vertical", "skip" are supported. Defaults to ('normal', \
          'horizontal', 'skip').
      action_prob (tuple): Corresponding action probabilities. Should be \
          the same length as action_candidate. Defaults to (1, 0, 0).
      scale (tuple): (min scale, max scale). Defaults to (0.8, 1.2).
      dx (int): The maximum x-axis shift will be (instance width) / dx.
          Defaults to 15.
      dy (int): The maximum y-axis shift will be (instance height) / dy.
          Defaults to 15.
      theta (tuple): (min rotation degree, max rotation degree). \
          Defaults to (-1, 1).
      color_prob (float): Probability of images for color augmentation.
          Defaults to 0.5.
      hflag (bool): Whether to use heatmap guided. Defaults to False.
      aug_ratio (float): Probability of applying this transformation. \
          Defaults to 0.5.
docstr: Restore the result of instaboost processing to the original anns
  format.
docstr_tok: ["Restore","the","result","of","instaboost","processing","to","the","original","anns","format","."]
code: def _parse_anns(
      self,
      results: dict,
      anns: list,
      ignore_anns: list,
      img: np.ndarray,
  ) -> dict:
      """Restore the result of instaboost processing to the original anns
      format."""
      instances = []
      for ann in anns:
          x1, y1, w, h = ann["bbox"]
          # TODO: more essential bug need to be fixed in instaboost
          if w <= 0 or h <= 0:
              continue
          bbox = [x1, y1, x1 + w, y1 + h]
          instances.append(
              dict(
                  bbox=bbox,
                  bbox_label=ann["category_id"],
                  mask=ann["segmentation"],
                  ignore_flag=0,
              )
          )
  
      instances.extend(ignore_anns)
      results["img"] = img
      results["instances"] = instances
      return results
  
file_path: mmdetection/mmdet/datasets/transforms/instaboost.py
imports: from typing import Tuple
  import numpy
  from mmcv.transforms import BaseTransform
  from mmdet.registry import TRANSFORMS
label_desc: Use image node_id 3 for calling the InstaBoost obj's underlying member method code with example usage: obj._parse_anns(results, anns, ignore_anns, img) and returns: results